Unlike the previous experiments, we do not perform model tuning and selection using AIC, due to the computational cost of fitting a large number of PDEs. Instead, we perform an achievability analysis, loosely inspired by [1]}, where we choose the algorithm parameters knowing the dynamics, to determine if it is even possible to learn a correct model given an appropriate model selection method. In particular, for MIOSR, we set the sparsity constraint to be the actual dimensionwise sparsity; for STLSQ, we try several thresholds slightly below the actual smallest coefficient; for E-STLSQ, we use library ensembling [2]} and take the \(k\)  terms with the highest inclusion probability, where \(k\)  is the true sparsity.
